华为智慧工地-安全帽检测（python算法源码+项目说明）.zip

# Extra ## ground-truth: - ### convert `xml` to our format: 1) Insert ground-truth xml files into **ground-truth/** 2) Run the python script: `python convert_gt_xml.py` - ### convert YOLO to our format: 1) Add class list to the file `class_list.txt` 2) Insert ground-truth files into **ground-truth/** 3) Insert images into **images/** 4) Run the python script: `python convert_gt_yolo.py` - ### convert keras-yolo3 to our format: 1) Add or update the class list to the file `class_list.txt` 2) Use the parameter `--gt` to set the **ground-truth** source. 3) Run the python script: `python3 convert_keras-yolo3.py --gt <gt_file_path>` 1) Supports only python 3. 2) This code can handle recursive annotation structure. Just use the `-r` parameter. 3) The converted annotation is placed by default in a new from_kerasyolo3 folder. You can change that with the parameter `-o`. 4) The format is defined according with github.com/qqwweee/keras-yolo3 ## detection-results: - ### convert darkflow `json` to our format: 1) Insert result json files into **detection-results/** 2) Run the python script: `python convert_dr_darkflow_json.py` - ### convert YOLO to our format: After runnuning darknet on a list of images, e.g.: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/test.txt > result.txt` 1) Copy the file `result.txt` to the folder `extra/` 2) Run the python script: `python convert_dr_yolo.py` - ### convert keras-yolo3 to our format: 1) Add or update the class list to the file `class_list.txt` 2) Use the parameter `--dr` to set the **detection-results** source. 3) Run the python script: `python3 convert_keras-yolo3.py --dr <dr_file_path>` 1) Supports only python 3. 2) This code can handle recursive annotation structure. Just use the `-r` parameter. 3) The converted annotation is placed by default in a new from_kerasyolo3 folder. You can change that with the parameter `-o`. 4) The format is defined according with github.com/gustavovaliati/keras-yolo3 ## Find the files that contain a specific class of objects 1) Run the `find_class.py` script and specify the **class** as argument, e.g. `python find_class.py chair` ## Intersect ground-truth and detection-results files This script ensures same number of files in ground-truth and detection-results folder. When you encounter file not found error, it's usually because you have mismatched numbers of ground-truth and detection-results files. You can use this script to move ground-truth and detection-results files that are not in the intersection into a backup folder (backup_no_matches_found). This will retain only files that have the same name in both folders. 1) Prepare `.txt` files in your `ground-truth` and `detection-results` folders. 2) Run the `intersect-gt-and-dr.py` script to move non-intersected files into a backup folder (default: `backup_no_matches_found`). `python intersect-gt-and-dr.py`